论文标题
在线无监督域改编的人重新识别
Online Unsupervised Domain Adaptation for Person Re-identification
论文作者
论文摘要
对人重新识别（人重新ID）的无监督域的适应性是将标记的源域上学习的知识转移到未标记的目标域中的任务。解决此问题的大多数文件都采用了脱机培训设置。更确切地说，假设我们可以访问完整的培训目标域数据集，则进行了重新ID模型的培训。在本文中，我们认为目标域通常由实用现实应用程序中的数据流组成，其中数据从不同的网络摄像机不断增加。 RE-ID解决方案还受到机密规定的约束，指出收集的数据只能存储在有限的时期内，因此该模型无法再访问先前看到的目标图像。因此，我们提供了一个新的但实用的在线环境，以针对人重新提供两个主要限制的人的无监督域适应：在线适应和隐私保护。然后，我们使用著名的Market-1501，Duke和MSMT17基准调整并评估此新在线设置上的最新UDA算法。
Unsupervised domain adaptation for person re-identification (Person Re-ID) is the task of transferring the learned knowledge on the labeled source domain to the unlabeled target domain. Most of the recent papers that address this problem adopt an offline training setting. More precisely, the training of the Re-ID model is done assuming that we have access to the complete training target domain data set. In this paper, we argue that the target domain generally consists of a stream of data in a practical real-world application, where data is continuously increasing from the different network's cameras. The Re-ID solutions are also constrained by confidentiality regulations stating that the collected data can be stored for only a limited period, hence the model can no longer get access to previously seen target images. Therefore, we present a new yet practical online setting for Unsupervised Domain Adaptation for person Re-ID with two main constraints: Online Adaptation and Privacy Protection. We then adapt and evaluate the state-of-the-art UDA algorithms on this new online setting using the well-known Market-1501, Duke, and MSMT17 benchmarks.
